Effects of temperature and humidity on cerebrovascular disease hospitalization in a super-aging society

Weather conditions influence the incidence of cardiovascular disease. However, few studies have investigated the association between weather temperature and humidity and cerebrovascular disease hospitalizations in a super-aging society. We included 606,807 consecutive patients with cerebrovascular disease admitted to Japanese acute-care hospitals between 2015 and 2019. The primary outcome was the number of cerebrovascular disease hospitalizations per day. Multilevel mixed-effects linear regression models were used to estimate the association of mean temperature and humidity, 1 day before hospital admission, with cerebrovascular disease hospitalizations, after adjusting for air pollution, hospital, and patient demographics. Lower mean temperatures and humidity < 70% or humidity ≧ 70% are associated with an increased incidence of cerebrovascular disease hospitalization (coefficient, − 1.442 [− 1.473 to − 1.411] per °C, p < 0.001, coefficient, − 0.084 [− 0.112 to − 0.056] per%, p < 0.001, and coefficient, 0.136 [0.103 to 0.168] per %, p < 0.001, respectively). Lower mean temperatures and extremely lower or higher humidity are associated with an increased incidence of cerebrovascular disease hospitalization in a super-aging society.


Association between weather conditions and cerebrovascular disease hospitalizations
Multilevel mixed-effects linear regression analysis indicated that many incident cerebrovascular disease hospitalizations were associated with lower average temperature (coefficient, − 1.442 [− 1.473 to − 1.411] per °C (P < 0.001) after adjustments for season, PM2.5, hospital, and patient characteristics (age, sex, height, weight, smoking, and Charlson Comorbidity Index) (Table 2).The association between mean temperature and mean humidity for specific risk estimates of cerebrovascular disease incidence is summarized in Fig. 1.The number of cerebrovascular disease hospitalizations was higher with temperatures < 7 °C (coefficient, − 3.405 [− 3.547 to − 3.263]) (Fig. 1a).
There was a non-linear relationship between humidity and the number of cerebrovascular disease hospitalizations (Fig. 1b).A negative linear association was found between mean humidity and cerebrovascular disease hospitalization at mean humidity < 70% (coefficient, − 0.084 [− 0.112 to − 0.056]).However, a positive linear association was found at mean humidity ≥ 70% (coefficient, 0.136 [0.103 to 0.168]).

Effect of weather condition indices in the subgroups
There was a non-linear relationship between temperature and the number of ischemic stroke hospitalizations (Fig. 2a).A negative linear association was found between mean temperature and ischemic stroke hospitalization at mean temperature < 7 °C (coefficient, − 2.098 [− 2.220 to − 1.976].However, a positive linear association was found at mean temperature ≥ 7 °C (coefficient, 0.190 [0.161 to 0.219]. There was a non-linear relationship between humidity and the number of cerebrovascular disease hospitalizations (Fig. 3a).A negative linear association was found between mean humidity and ischemic stroke hospitalization at mean humidity < 40% (coefficient, 0.165 [0.151 to 0.179].However, a positive linear association was found at mean humidity ≥ 40% (coefficient, 0.136 [0.103 to 0.168]).

Discussion
We present a contemporary analysis of nationwide data involving 606,807 patients and describe the estimated number of hospitalizations for incident cerebrovascular disease.Overall, a higher number of incident cerebrovascular hospitalizations was associated with (1) lower mean temperatures and (2) extremely lower or higher humidity.These findings highlight the importance of assessing lower mean temperatures and extreme humidity ranges as potential indicators for cerebrovascular disease risk, particularly in a super-aging society like Japan.
We found that lower mean temperatures were associated with increased hospitalization for cerebrovascular disease.Other studies have shown that cold-induced systemic hypertension 10 is a risk factor associated with the renin-angiotensin system and sympathetic nerve activity, which could modulate the incidence of cerebrovascular disease hospitalization.Vascular compression of the rostral ventrolateral medulla (RVLM) is a known cause of www.nature.com/scientificreports/hypertension 11,12 .Patients with RVLM vascular compression have shown greater variability in blood pressure during ischemic stroke 13 .Also, lower temperatures may be an environmental factor that causes a higher incidence of hospitalization.Extremely lower or higher humidity was associated with increased hospitalizations due to cerebrovascular disease.However, a systematic review showed no association between daily humidity and cerebrovascular disease occurrence 14 .A previous study revealed that extremely lower or higher humidity correlated with the incidence of stroke 15 .High humidity can cause dehydration, which increases the risk of thrombosis.However, these influences may not be as strong as those of other weather conditions, such as temperature, which may be responsible for physiological changes that could increase stroke risk 16 .These include increased blood pressure, erythrocyte  and thrombocyte counts, and blood viscosity during cold weather 17 .Plasma fibrinogen concentrations are also higher in older patients, especially during a cold weather 18 .This may partly explain why humidity is not associated with stroke occurrence.These findings may be influenced by differences in the cerebrovascular disease type.Ischemic stroke has a higher incidence rate than cerebral or subarachnoid hemorrhage 19 .Our study might also show ischemic stroke as the main finding that revealed an association between a lower mean temperature, extremely lower or higher humidity, and hospitalization due to ischemic stroke.These results were consistent with previous reports 15 .
Elderly individuals tend to spend more time indoors due to factors such as illnesses and decreased physical function, compared to younger individuals.Therefore, there is a possibility that the impact of temperature differences may diminish with indoor activities.Nevertheless, limited research has compared indoor time between the elderly and younger age groups.Considering that the risk of illness is higher in the elderly, it is unclear whether temperature differences indoors have a significant effect on disease incidence.Although studies have reported differences in indoor and outdoor temperature and humidity 20 , these differences are generally consistent with seasonal trends.Specifically, in the context of Japan, it is important to note that regional variations and the presence or absence of heating and cooling systems can also influence these differences.However, there is currently no comprehensive report that has investigated these differences across all regions of Japan.www.nature.com/scientificreports/One of the limitations of our study is that we included only Japanese diagnosis procedure combination hospitals with cardiovascular beds that meet the JCS requirements.However, the JROAD is the largest retrospective study of nationwide cardiac health outcomes in Japan and constitutes a comprehensive database of epidemiological data for population-based studies.Two, retrospective studies do not determine cause and effect; however, the large sample size is a noteworthy strength of this study.Three, When patients are exposed to weather conditions different than that of the hospital they are admitted in, it could introduce bias in the results.Furthermore, this study does not include data on specific weather conditions observed at the research site, which could affect the outcomes.Finally, the predictor of cerebrovascular disease was not adjusted for hypertension.

Conclusion
Lower mean temperatures and extremely lower or higher humidity are associated with an increased incidence of cerebrovascular disease hospitalization in the aging Japanese society.The findings of this study can provide guidance to healthcare providers on advising patients to monitor weather, temperature, and humidity in a country with an increasingly aging society.

Data collection
The JROAD database is a nationwide retrospective registry.The database was designed to assess the clinical activity of each Japanese institution regarding cardiovascular care and to provide adequate feedback to teaching hospitals for improving patient care.A detailed description of the database design and methods has been previously published 21 .The JCS developed the JROAD database, which includes the demographics of each hospital since 2004.The JCS also developed the JROAD-DPC nationwide database, which includes data from the Japanese diagnosis procedure combination/per diem payment system (DPC/PDPS) since 2014.The DPC database is a mixed-case classification system linked with a lump-sum payment system, launched in 2002 by the Japanese Ministry of Health, Labour and Welfare 22 .Compared with other registry databases, the Japanese DPC database enables researchers to conduct nationwide descriptive and/or analytical epidemiology studies in a real-world clinical setting.The JROAD database started to collect data about cerebrovascular disease in 2015.The DPC database includes data on the following elements: demographics for each patient (e.g., age and sex); principal diagnoses (coded according to the International Classification of Diseases, 10th revision [ICD-10]); comorbidities at admission (ICD-10 coded); complications after admission (ICD-10 coded); procedures, including surgery, medications, and devices used during hospitalization; length of stay; discharge status; and medical expenses [21][22][23][24] .Various institutions using the DPC system include academic, urban, and rural hospitals 21,24 .All data included in this study were from hospitalized patients with clinically apparent cardio-cerebrovascular disease.We collated and used a dataset of weather variables in Japan from the Japan Meteorological Agency (https:// www.jma.go.jp/ jma/ indexe.html).The weather variables were daily weather temperatures and humidity, including maximal and minimum values.The humidity was measured as relative humidity.Localized weather data were obtained from the weather station closest to each hospital; as the hospitals were located in all 47 prefectures of Japan, weather data were obtained from all prefectural capitals.We searched the sites of weather stations by the municipality code, which the Ministry of Internal Affairs and Communications in Japan assigns to each municipality specifically.We combined the hospital sites with the monitoring stations in each prefecture to create a unified dataset.The weather variables were merged with the DPC database using the acute hospitalization day and the municipal code provided by the Japanese Ministry of Internal Affairs and Communications (https:// www.soumu.go.jp/ densh ijiti/ code.html).We also collated and used a dataset of air pollution variables in Japan from the National Institute for Environmental Studies (http:// www.nies.go.jp/ db/ index-e.html).The air pollution variables included hourly PM2.5.
This study included 715 hospitals and 4,998,541 consecutive patients admitted during the study period.We collected data from patients with cardio-cerebrovascular disease who required hospitalization.The exclusion criteria were as follows: cardiovascular disease hospitalization, planned hospitalization, missing temperature or humidity data, and missing patient characteristic data.After excluding 4,391,734 patients, a total of 606,807 patients with cerebrovascular disease were included in the analysis (Fig. 4).
The study was conducted in accordance with the principles of the Declaration of Helsinki.The author designed the present study, and the study protocol was approved by the Institutional Ethics Committee of St. Marianna University of Medicine.Each hospital anonymized the patient IDs using code-change equations for the original JROAD-DPC data.The requirement for individual informed consent was waived by the institutional ethics committee of St. Marianna University of Medicine because all data were anonymized when provided by the DPC.The data was sent to the Ministry of Health, Labor, and Welfare, Japan Meteorological Agency, and the National Cerebral Cardiovascular Center managed the database.The National Cerebral and Cardiovascular Center notified the patients that their information was being collected for this study through homepages or posters at each hospital.The patients could choose to exclude this information.This study did not investigate blood tests or electrocardiography data because we used only DPC data.The method of this study cited from previous our study 6,7 .
Cerebrovascular disease included ischemic stroke, cerebral hemorrhage, and subarachnoid hemorrhage.These diseases were assigned the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) code, which is recorded with the "main diagnosis, admission precipitating diagnosis, most resource-consuming diagnosis, " or "second-most resource-consuming diagnosis." The number of cerebrovascular diseases overlapped in the subgroups.Ischemic stroke was assigned the ICD-10 code I63.Cerebral hemorrhage was assigned the ICD-10 code I61.Subarachnoid hemorrhage was assigned the ICD-10 code I60.The seasons were divided into spring (March-May), summer (June-August), autumn (September-November), and winter www.nature.com/scientificreports/(December-February).The mean temporaries were defined as the mean hourly temperature and humidity within a day.The weather variables on a certain day were assigned to the day before the emergency hospitalization for cerebrovascular disease.This is because hospitalization times were not available in the database.The cutoff values associated with the results were visually determined by an author (S.D) from the graph.

Design
We conducted a retrospective study using data from JROAD and JROAD-DPC and weather variables between April 1, 2015, and March 31, 2019.

Outcomes
The primary outcome was the number of cerebrovascular diseases requiring hospitalization per day.

Covariates
The mean weather temperature and humidity, as continuous variables, were adjusted for season, PM2.5, hospital demographics (East/West Japan, number of hospital beds, presence of a coronary care unit, cardiac surgery service, and board-certified cardiologist), and patient demographics (age, sex, height, weight, smoking, and Charlson Comorbidity Index).The Charlson Comorbidity Index has been developed and validated to predict the risk of mortality in longitudinal studies 25 .The score can be calculated from a weighted index consisting of age and the number and severity of comorbid diseases.

Statistical analysis
Patient characteristics are expressed as medians and interquartile ranges for continuous variables.Categorical variables are presented as frequencies (%).We used multilevel mixed random-effects and population-averaged linear models to evaluate the association between the number of cerebrovascular disease hospitalizations and weather variables.Multilevel mixed-effects models were used to evaluate the random effects of hospital variations (institutional codes) using "xtset" command in STATA.Experienced cardiologists considered these covariates clinically important factors.As continuous variables, the weather temperature and humidity were adjusted for covariates.Linearity was checked for continuous and categorical variables using STATA multivariate regression spline (MVRS) command.MVRS selects the regression spline model that best predicts the outcome variable.The MVRS indicated a linear spline relationship with humidity.The cutoff value of humidity was determined based on the knots calculated by MVRS and was tested using "mkspline" command in STATA.The mk-spline creates variables that contain a linear spline.These interactions were examined in each season.All analyses were performed using the STATA statistical software version 14 (Stata Corp., College Station, TX, USA).Statistical significance was defined as p < 0.05.

Figure 1 .
Figure 1.Association between mean temperature or humidity and cerebrovascular disease hospitalizations.(A) Linearity was checked for continuous and categorical variables using STATA's multivariable regression splines (MVRS) command.MVRS indicated a non-linear relationship with mean temperature.The predicted number of cerebrovascular diseases per day was univariate.The x-axis represents temperature (°C) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.(B) MVRS indicated a non-linear relationship with humidity.The predicted number of cerebrovascular diseases per day was univariate.The x-axis represents humidity (%) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.

Figure 2 .
Figure 2. Association between mean temperature and subgroups of cerebrovascular disease hospitalizations.(A) Linearity was checked for continuous and categorical variables using STATA's multivariable regression splines (MVRS) command.MVRS indicated a non-linear relationship with mean temperature.The predicted number of ischemic stroke hospitalizations per day was univariate.The x-axis represents temperature (°C) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.(B) MVRS indicated a nonlinear relationship with temperature.The predicted number of cerebral hemorrhage hospitalizations per day was univariate.The x-axis represents temperature (°C) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.(C) MVRS indicated a non-linear relationship with temperature.The predicted number of cerebral hemorrhage hospitalizations per day was univariate.The x-axis represents temperature (°C) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.

Figure 3 .
Figure 3. Association between mean humidity and subgroups of cerebrovascular disease hospitalizations.(A) Linearity was checked for continuous and categorical variables using STATA's multivariable regression splines (MVRS) command.MVRS indicated a non-linear relationship with mean humidity.The predicted number of ischemic stroke hospitalizations per day was univariate.The x-axis represents humidity (%) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.(B) MVRS indicated a nonlinear relationship with humidity.The predicted number of cerebral hemorrhage hospitalizations per day was univariate.The x-axis represents humidity (%) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.(C) MVRS indicated a non-linear relationship with humidity.The predicted number of cerebral hemorrhage hospitalizations per day was univariate.The x-axis represents humidity (%) as a continuous variable.The solid and dashed lines indicate 95% confidence intervals.

Figure 4 .
Figure 4. Flowchart of patient disposition.A total of 606,807 patients who underwent cerebrovascular disease hospitalization from 715 hospitals were included.The patients with planned hospitalization were excluded because only patients with acute hospitalized cerebrovascular disease were included for analysis.The patients with cardiovascular disease were also excluded.

Table 1 .
Baseline characteristics.The number of cerebrovascular diseases overlapped in subgroups.IQR, interquartile range.

Table 2 .
Association of the number of cerebrovascular disease hospitalization with temperature and humidity.
Coefficients represent the change in the number of cerebrovascular disease hospitalization per 1 unit increase in mean temperature or humidity, with adjustments for multiple variables: season, number of hospital beds, coronary care units, cardiac surgery, East/West Japan, age, sex, height, weight, Brinkmann Index, and Charlson Comorbidity Index score.Multilevel mixed-